High-throughput prediction of PFAS binding affinities with human liver-fatty acid binding protein using machine learning and QSAR model
Year: 2025
Type: article
Abstract: • K d FABP values of PFAS were measured using an ultrafiltration method. • Suspect screening expanded PFAS structural diversity from environmental samples. • Machine learning based QSAR predicted K d FABP of 9117 EPA-listed and 76,216 AI-derived PFAS. • Structural and physicochemical drivers of FABP binding were identified. The binding affinity with human liver-fatty acid binding protein ( K d FABP ) is a key parameter to characterize the accumulation potential of per- and polyfluoroalkyl substances (PFAS) in animal liver. However, the vast number of PFAS, combined with the limited available commercial standards, makes it a major challenge to measure their K d FABP . In this study, the K d FABP of 44 PFAS standards, and 72 PFAS extracted and semi-quantified in environmental samples by suspect screening analysis were measured using ultrafiltration methods. Several machine learning regression algorithms were developed to predict the K d FABP , and extreme gradient boosting regression exhibited the best performance. Of note, intrinsic molecular descriptors, such as AATS0d, AATS8pe, VR1_A, ATSC1d, and AATSC2Z were found to be the primary factors to affect the binding affinities. The optimized model was then applied to predict the K d FABP values of 9117 PFAS listed by U.S. EPA. DTXSID40896722, which features perfluorinated branches connected through sulfonyl linkages, exhibits the lowest K d FABP value. Additionally, by combining the predicted K d FABP of 76,216 artificial-intelligence-generated PFAS, it was found that chemical fragments containing carbon–fluorine and ether moieties are conducive for the binding. This study holds significant importance in de novo design of environmentally friendly PFAS.
Source: Environment International
Authors: Yibo Jia, Rouyi Wang, Yumin Zhu, Lingyan Zhu
Institution: Nankai University
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,750
Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Ministry of Science and Technology of the People's Republic of China, Nankai University, National University's Basic Research Foundation of China, Chinese Universities Scientific Fund, Fundamental Research Funds for the Central Universities
OpenAlex ID: W4416754718